class: center, middle, inverse, title-slide # SIVOCS | Preliminary Results of the Survey Analysis ### --- <style> .center2 { margin: 0; position: absolute; top: 50%; left: 50%; -ms-transform: translate(-50%, -50%); transform: translate(-50%, -50%); } .large { font-size: 130% } .small { font-size: 70% } .remark-slide-content.hljs-default { border-top: 60px solid #23373B; } .remark-slide-content > h1 { font-size: 30px; margin-top: -75px; } </style> # Sample, Respondents & Bias .pull-left[ <img src="data:image/png;base64,#./table_2.png" width="100%" /> <br> <br> * Slight bias towards female participants * Slight bias towards SSH and Natural Sciences ] .pull-right[ <img src="data:image/png;base64,#../sample_respondent.svg" width="200%" /> ] --- # Familiarity with SI and Transdisciplinarity .right[.small[N={352, 112, 360}]] <br> <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-6-1.svg" width="864" style="display: block; margin: auto;" /> <br> * *Experience with transdisciplinary research* and *projct's contribution to SI* have relatively similar distributions, over 45 % of respondents rated equal or higher than 7 for each. * *Familiarity with SI* generally assessed lower, i.e. over 60 % of respondents rated between 0 and 3. --- # *Familiarity with SI* across scientific domains <br> <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-9-1.svg" width="864" style="display: block; margin: auto;" /> <br> <br> * *Familiarity with SI* differs across scientific domains (ANOVA test: p < 0.05) * *Biology and Medicine* and *Math., Natur, and Eng. Sci.* are similar (pairwise t-test: p > 0.05) * *Humanities and Social Sciences* are significantly different than the others (pairwise t-test with each: p < 0.05) --- # Intention & Agency: Motivation .right[.small[N = {360, 354, 365}]] <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-10-1.svg" width="1152" style="display: block; margin: auto;" /> <br> * Most projects (84%) driven by academic motivation (better understanding a phenomenon), * followed by 62 % strongly motivated to directly address a problem; * *Improving the human condition/welfare* more balanced across answer options --- # Intention & Agency – Impulses from the Non-academic World .pull-left[ ###### *Addressing a specific problem* ... <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-13-1.svg" width="504" /> ] .pull-right[ ###### Direct benef for the general population or a specific non-academic group <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-14-1.svg" width="504" /> ] * *Medical/health* problems followed by *societal* ones are most often mentioned *impulses from the non-acadmic world* * *Economic* problems least frequently mentioned category (only 29 participants) --- # Transdisciplinary Aspects: Inter-/Transdisciplinary Involvement .right[.small[N = 360]] <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-15-1.svg" width="1024" style="display: block; margin: auto;" /> * Interdisciplinary involvement common: researchers from other disciplines quite centrally involved (41 %) * Transdisciplinary involvement + not as central but noteworthy for fundamental research projects + similar across different groups in terms of extent and depth of involvement (*marginal* / *central*) --- # Transdisciplinary Aspects: Nature of Involvement .right[.small[N={74, 80, 82, 51, 94, 114}]] <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-18-1.svg" width="1024" style="display: block; margin: auto;" /> * Transdisciplinary involvement mostly *consultative* or *contributory* in nature. * *Collaborative* processes more likely in projects where *welfare-/education providing institutions* or *company/ business representatives* are relevant --- # Transdisciplinary Aspects: Target Group Goals ##### *Your project has* ... <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-19-1.svg" width="1152" /> * *Empowering people* most frequently selected (170) category among goals for target group * *Enabling diversity and exchange of different perspectives* second most freqeuntly selected (151) * *Worked towards improving people lives* least selected category (167) --- # Regulatory Framework: Open Science Concepts ###### Norms, requirements, practices applicable to research projects: <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-20-1.svg" width="1152" /> * *Open access to publications* most frequently selected category (326 times), * followed by *open access to data* (234 times) --- # Regulatory Framework: Gender Dimension & Support for Policy-Making .pull-left[ ###### Explicit consideration of gender in research <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-21-1.svg" width="504" /> * Vast majority of participants (275) stated *gender* was no explicit consideration in their project. ] .pull-right[ ###### Aim: support of evidence-based policy-making <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-22-1.svg" width="504" /> * ~ a third of projects aimed at supporting evidence-based policy-making ] --- # Outcome-orientation: Direct Contribution .right[.small[N = 360]] <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-23-1.svg" width="864" /> * A tiny minority directly contributed to *new or better services, products, or processes* for civil society organisations * However, 18 % of respondents stated to have strongly contributed to benefit the general population --- # Outcome-orientation: Intended Effects ###### Intended short- or long-term change benefiting specific target groups or the general population: <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-26-1.svg" width="960" style="display: block; margin: auto;" /> * Improving the *understanding* in the general population as well as raising the *awareness* by far the most frequently selected category (79 and 50 times) * *Changing behaviour* most frequently stated for policy-makers/public administrators --- # Outcome-orientation: Impact Statements .right[.small[N = {172, 149, 140}]] <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-27-1.svg" width="1152" style="display: block; margin: auto;" /> * Academic dimension most frequently chosen, * followed by *issues not widely known in society* and *generate a deeper understanding of a specific social issues*. --- --- # Correlation Matrix: Impact Assessment vs. Motivation .small[**mot.**: Motivation to *understand a phenomenon* or *directly address a problem* (natural, technical, economic, or social) **impactGr.**: direct contribution to *new or better services, products, processes, or ways of doing things* that were targeted towards ...] .pull-left[ <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-29-1.svg" width="504" /> ] .pull-right[ * *Motivations* do not correlate highly with *direct contribution to new or better services, products, processes, or ways of doing things* by projects * *Motivation to improve human condition* shows significant correlations with almost all impact statements (excl. purely academic contribution) * Strongest positive correlation is between *the benefit to general population* and *motivation to improve human condition* (0.5) ] --- # Further steps ##### Ongoing analytical tasks * Statistical analyses of relationships between variables / correlations * Linear and logistic regression models * Dimension reduction, factor analysis, principal component analysis ##### Outlook * Build statistical models to predict "degree" of SI * Compile analytical results of survey into a report (part of the final report) * Ideally: test with more quantitative data * Liaise with SNSF to shape messages to target audiences in 2022 * Publish results in scientific journal (with approval of SNSF) --- class:clear <br> <br> <br> .center[ ### Many thanks for your attention! ] Contact information: * Utku Demir
* Dietmar Lampert
(presenter) * Klaus Schuch
ZSI - Centre for Social Innovation - https://zsi.at/en --- # Familiarity with SI and Transdisciplinarity vs Impact Assessment, Correlation Matrix .small[**involv.**: In your research processes, did you actively involve one or more of the following groups(*)? **policy.uptake**: From your perspective, to what extent were project results taken up by policy-making and/or public administration and/or governmental agencies? **scalability**: How would you assess in the long term the scalability of the results generated by your project? ] .pull-left[ <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-32-1.svg" width="504" /> ] .pull-right[ * There is almost no stat. significant correlations between up-, out-scalability and inter-/transdisciplinary involvement. * Deep-scalability correlates with several of the trans-/disciplinary involvement with the highest being the involvement of citizens (0.4). * Policy uptake also correlates relatively strongly with transdisciplinary involvement in the project, especially when policymakers and civil society organisations involved. ] --- # Correlation Matrix: Motivation Types vs Trans-, Interdisciplinary Involvement .small[ **involv.**: In your research processes, did you actively involve one or more of the following groups(*)? **mot.**: When you designed your project, to what degree were you motivated to… ] .pull-left[ <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-33-1.svg" width="504" /> ] .pull-right[ * Motivation to *understand a natural, technical, economic, or social phenomenon better* and *directly address a natural, technical, economic, or social problem* do not necessarily correlate with inter-/transdisciplinary involvements. * Although weakly, motivation to improve human condition/welfare correlates with all of the inter-/transdisciplinary involvements with the strongest ones being *civil society* and *civil society* organisations. ] --- # Transdisciplinary Experience vs. Contribution to SI by Sciencetific Domains <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-34-1.svg" width="1152" /> --- # Scaleability vs. Transdisciplinarity by Scientific Domains <img src="data:image/png;base64,#14_SNF_presentation_files/figure-html/unnamed-chunk-35-1.svg" width="1152" /> ---